I.Setup
Libraries needed: [Note: I wrote results hide but the ## are not results maybe??]
library(dplyr)##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, unionlibrary(Seurat)## Attaching SeuratObjectlibrary(patchwork)Loading data & Checking out how the loaded data looks like:
mouse.data <- Read10X(data.dir = "/home/xiaoyi/Documents/Praktikum/Bioinformatik/GSE113854_RAW")
#mouse.data <- Read10X(data.dir = "/Users/xiaoyizheng/Downloads/Praktikum/Bioinfo_Einarbeiten/CellChatSelf/mouse/GSE113854_RAW/")
mouse.data## [[ suppressing 32 column names 'AAACCTGAGAATTCCC-1', 'AAACCTGAGACCGGAT-1', 'AAACCTGAGACGCACA-1' ... ]]
## [[ suppressing 32 column names 'AAACCTGAGAATTCCC-1', 'AAACCTGAGACCGGAT-1', 'AAACCTGAGACGCACA-1' ... ]]
## [[ suppressing 32 column names 'AAACCTGAGAATTCCC-1', 'AAACCTGAGACCGGAT-1', 'AAACCTGAGACGCACA-1' ... ]]Creating Seurat object:
mouse <- CreateSeuratObject(counts = mouse.data, project = "MoSk", min.cells = 3, min.features = 200)II. Pre-processing
QC (quality control) Matrix for selection and filtration of cells, using user-defined criteria. In this case it is the mitochondrial genes as criteria. [note: apparently they all have same level of mt content. Maybe pre-processed already? or maybe that is not the proper criteria to choose in this case. Which one could be better instead of it?]
mouse[["percent.mt"]] <- PercentageFeatureSet(mouse, pattern = "^MT-")
#Visualization by violin plot (mt percent no significance):
VlnPlot(mouse, features = c("nFeature_RNA", "nCount_RNA", "percent.mt"), ncol = 3) ## Warning in SingleExIPlot(type = type, data = data[, x, drop = FALSE], idents =
## idents, : All cells have the same value of percent.mt.
Genes and total molecules calculated and stored in obj. meta data.
Showing QC metrics for the first 5 cells:
head(mouse@meta.data, 5) ## orig.ident nCount_RNA nFeature_RNA percent.mt
## AAACCTGAGAATTCCC-1 MoSk 1475 783 0
## AAACCTGAGACCGGAT-1 MoSk 4148 1624 0
## AAACCTGAGACGCACA-1 MoSk 3136 1342 0
## AAACCTGAGATGTGGC-1 MoSk 3138 1323 0
## AAACCTGAGCTGCGAA-1 MoSk 4029 1721 0#examining a few genes in the first thirty cells
mouse.data[c("Igf2", "Cck", "Pf4"), 1:30] ## 3 x 30 sparse Matrix of class "dgCMatrix"## [[ suppressing 30 column names 'AAACCTGAGAATTCCC-1', 'AAACCTGAGACCGGAT-1', 'AAACCTGAGACGCACA-1' ... ]]##
## Igf2 . . . . . 1 . . . . . . . . . . . 1 . . . . . . . . . . . .
## Cck . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
## Pf4 . . . . . . . . 11 . . . . . 25 . . 1 1 . 1 1 . 1 . . . . . 2#a few genes in the first 30 cells
#picked "Igf2", "Cck", "Pf4" according to webVisualization of feature-feature relationships
plot1 <- FeatureScatter(mouse, feature1 = "nCount_RNA", feature2 = "percent.mt") ## Warning in cor(x = data[, 1], y = data[, 2]): the standard deviation is zero#since all have same mt quality...
plot2 <- FeatureScatter(mouse, feature1 = "nCount_RNA", feature2 = "nFeature_RNA")
#positively correlated
plot1 + plot2
III. Normalization
mouse <- NormalizeData(mouse, normalization.method = "LogNormalize", scale.factor = 10000)
#alt.: mouse<- NormalizeData(mouse)IV. Feature Selection
mouse <- FindVariableFeatures(mouse, selection.method = "vst", nfeatures = 2000)
top10 <- head(VariableFeatures(mouse), 10) # Identify the 10 most highly variable genes
top10## [1] "Hbb-bs" "Hba-a1" "Ccl21a" "Hbb-bt" "Saa3" "Acp5" "Ccl5" "S100a8"
## [9] "Mmp9" "S100a9"top20 <- head(VariableFeatures(mouse), 20) # Identify the 20 most highly variable genes
top20## [1] "Hbb-bs" "Hba-a1" "Ccl21a" "Hbb-bt" "Saa3" "Acp5" "Ccl5" "S100a8"
## [9] "Mmp9" "S100a9" "Cma1" "Cd74" "Mmp3" "Ccl17" "Pf4" "Apoe"
## [17] "Lgals7" "Ctla2a" "Mgp" "Hp"plot1 <- VariableFeaturePlot(mouse)
plot2 <- LabelPoints(plot = plot1, points = top10, repel = TRUE)## When using repel, set xnudge and ynudge to 0 for optimal resultsplot1 + plot2
V. Scaling
all.genes <- rownames(mouse)
mouse <- ScaleData(mouse, features = all.genes)## Centering and scaling data matrix#optional: mouse <- ScaleData(mouse)VI. Linear Dimension Reduction
Perform PCA
mouse <- RunPCA(mouse, features = VariableFeatures(object = mouse))## PC_ 1
## Positive: Fth1, Tyrobp, Fcer1g, Lyz2, Ftl1, H2-D1, Tmsb4x, Ctss, C1qb, Apoe
## C1qa, C1qc, Ptpn18, Wfdc17, Cd52, Pf4, Laptm5, Lgals3, Ms4a7, Aif1
## Cd74, Clec4n, Ucp2, Lst1, Ms4a6c, Cd68, Id2, Alox5ap, Cd14, Fxyd5
## Negative: Col1a1, Col1a2, Col3a1, Sparc, Bgn, Col5a2, Dcn, Meg3, Postn, Col6a1
## Col6a2, Fstl1, Col12a1, Col5a1, Aebp1, Lum, Serpinh1, Mfap4, Col6a3, Mmp2
## Thbs2, Gpx3, Fbln2, Serpinf1, Tmsb10, Fn1, Lgals1, Lrrc15, Itm2a, Loxl1
## PC_ 2
## Positive: Tyrobp, Fcer1g, Lyz2, Lgals3, C1qb, Ctss, Apoe, Ftl1, C1qa, Fth1
## C1qc, Pf4, Wfdc17, Ms4a7, Cd52, Lst1, Aif1, Cd74, Cstb, Laptm5
## Lgals1, Clec4n, Id2, Ifi27l2a, Cd68, Ms4a6c, Alox5ap, Cd14, Csf1r, Cyba
## Negative: Col4a1, Col4a2, Cdh5, Igfbp7, Pecam1, Egfl7, Crip2, Sparcl1, Plvap, Ramp2
## Col15a1, Col18a1, Cav1, Ctla2a, Emcn, Myct1, Kdr, Adgrf5, Adgrl4, Cd93
## Esam, Mcam, Ly6c1, Pdlim1, Cldn5, Fabp4, Mmrn2, Eng, Gng11, Aqp1
## PC_ 3
## Positive: Cdh5, Pecam1, Ctla2a, Egfl7, Cd93, Ramp2, Cd34, Myct1, Kdr, Ly6c1
## Mfap4, S100a16, Igf1, Adgrl4, Emcn, Lum, Igfbp3, Mmrn2, Mest, Cldn5
## Ly6a, Ptn, Fbln2, Plvap, Aebp1, Tie1, Gja1, Ptprb, Gpihbp1, Rbp1
## Negative: Rgs5, Ndufa4l2, Gm13889, Higd1b, Des, Notch3, Serpine2, Cox4i2, Il6, Acta2
## Mylk, Cygb, Rasgrp2, Ebf1, Ppp1r14a, Mgp, Thy1, Crip1, Col4a1, Mustn1
## Pdgfa, Myl9, S100a4, Phlda1, Abcc9, Tppp3, Col4a2, Gucy1a3, Parm1, Actg2
## PC_ 4
## Positive: Malat1, Il6, Cxcl1, Meg3, Serping1, Mt1, Gm13889, Rgs5, Col14a1, Sparcl1
## Errfi1, Ogn, Eln, Mt2, Nov, Neat1, Klf4, Clec3b, Ccl2, Col4a1
## Fmod, Procr, Ebf1, Col4a2, Fxyd1, H19, Id3, Tinagl1, Cygb, Fosb
## Negative: 2810417H13Rik, Birc5, Ube2c, Stmn1, Top2a, Tuba1b, Cenpa, Hmgb2, Cdca3, H2afz
## Cks2, Ccnb2, Prc1, Cks1b, Ccna2, 2700094K13Rik, Cenpf, Cdk1, Smc2, Ccnb1
## Cdca8, Mki67, Nusap1, Tpx2, Lockd, Tubb5, Spc24, Tk1, H2afx, Cenpm
## PC_ 5
## Positive: Pf4, Apoe, Sepp1, C1qc, C1qb, C1qa, Cxcl1, Neat1, Ctsd, Ms4a7
## Lyz2, Fosb, Klf4, Lgmn, Gas6, Cxcl2, Ier3, Syngr1, Ccl7, Mrc1
## H19, Ccl2, Ftl1, Igfbp4, Col14a1, Trem2, Igf1, Lst1, Eln, Slc40a1
## Negative: Tmsb10, S100a6, Ccr7, H2-Eb1, Ramp3, Samsn1, Crabp1, H2-Aa, H2-Ab1, Tmsb4x
## Klrd1, Icos, Bcl2a1d, Ptprcap, Napsa, Tbc1d4, Ifitm1, Cd209a, Cytip, Tnfrsf18
## AW112010, Cd52, H2-DMb1, H2-DMa, Cd74, Il1r2, Cd3d, Cd7, Tnfrsf4, Tnfrsf9Examine and visualize PCA results a few different ways
print(mouse[["pca"]], dims = 1:5, nfeatures = 5)## PC_ 1
## Positive: Fth1, Tyrobp, Fcer1g, Lyz2, Ftl1
## Negative: Col1a1, Col1a2, Col3a1, Sparc, Bgn
## PC_ 2
## Positive: Tyrobp, Fcer1g, Lyz2, Lgals3, C1qb
## Negative: Col4a1, Col4a2, Cdh5, Igfbp7, Pecam1
## PC_ 3
## Positive: Cdh5, Pecam1, Ctla2a, Egfl7, Cd93
## Negative: Rgs5, Ndufa4l2, Gm13889, Higd1b, Des
## PC_ 4
## Positive: Malat1, Il6, Cxcl1, Meg3, Serping1
## Negative: 2810417H13Rik, Birc5, Ube2c, Stmn1, Top2a
## PC_ 5
## Positive: Pf4, Apoe, Sepp1, C1qc, C1qb
## Negative: Tmsb10, S100a6, Ccr7, H2-Eb1, Ramp3VizDimLoadings(mouse, dims = 1:2, reduction = "pca")
DimPlot(mouse, reduction = "pca")
heatmap: exploration of the primary sources of heterogeneity -> decide which PCs to include for further downstream analyses
DimHeatmap(mouse, dims = 1, cells = 500, balanced = TRUE) #heatmap: exploration of the primary sources of heterogeneity
DimHeatmap(mouse, dims = 1:15, cells = 500, balanced = TRUE)
VII. Determination of Dimension
Metafeature: combines information across a correlated feature set. top principal components => robust compression how many PCs to take? ###JackStraw procedure permute subset of the data (1% by default) and rerun PCA significant PCs ~ strong enrichment of low p-value features
mouse <- JackStraw(mouse, num.replicate = 100)
mouse <- ScoreJackStraw(mouse, dims = 1:20)JackStrawPlot(mouse, dims = 1:15)## Warning: Removed 21000 rows containing missing values (`geom_point()`).
alternative heuristic method Elbow plot: observe where the elbow is and then see which PCs to choose
ElbowPlot(mouse)
# VIII. Clustering Cells
mouse <- FindNeighbors(mouse, dims = 1:10)## Computing nearest neighbor graph## Computing SNN#mouse <- FindClusters(mouse, resolution = 0.5)
mouse <- FindClusters(mouse, resolution = 0.3) ## Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
##
## Number of nodes: 22322
## Number of edges: 716976
##
## Running Louvain algorithm...
## Maximum modularity in 10 random starts: 0.9309
## Number of communities: 15
## Elapsed time: 3 seconds#chosen resolution 0.3 here for retrieving 13 clusters 0-12
# Look at cluster IDs of the first 5 cells
head(Idents(mouse), 5)## AAACCTGAGAATTCCC-1 AAACCTGAGACCGGAT-1 AAACCTGAGACGCACA-1 AAACCTGAGATGTGGC-1
## 3 9 8 0
## AAACCTGAGCTGCGAA-1
## 5
## Levels: 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14IX. Non-linear Dimension Reduction
e.g. UMAP, tSNE
input to the UMAP and tSNE: same PCs as input to the clustering analysis
mouse <- RunUMAP(mouse, dims = 1:10)## Warning: The default method for RunUMAP has changed from calling Python UMAP via reticulate to the R-native UWOT using the cosine metric
## To use Python UMAP via reticulate, set umap.method to 'umap-learn' and metric to 'correlation'
## This message will be shown once per session## 14:24:47 UMAP embedding parameters a = 0.9922 b = 1.112## 14:24:47 Read 22322 rows and found 10 numeric columns## 14:24:47 Using Annoy for neighbor search, n_neighbors = 30## 14:24:47 Building Annoy index with metric = cosine, n_trees = 50## 0% 10 20 30 40 50 60 70 80 90 100%## [----|----|----|----|----|----|----|----|----|----|## **************************************************|
## 14:24:49 Writing NN index file to temp file /tmp/Rtmp8EeFf6/file2e6e545a3099
## 14:24:49 Searching Annoy index using 1 thread, search_k = 3000
## 14:24:56 Annoy recall = 100%
## 14:24:56 Commencing smooth kNN distance calibration using 1 thread with target n_neighbors = 30
## 14:24:56 Initializing from normalized Laplacian + noise (using irlba)
## 14:24:57 Commencing optimization for 200 epochs, with 918564 positive edges
## 14:25:05 Optimization finished#note that you can set `label = TRUE` or use the LabelClusters function to help label individual clusters
DimPlot(mouse, reduction = "umap")
#saveRDS(mouse, file = "/Users/xiaoyizheng/Downloads/Bioinfo_Einarbeiten/CellChatSelf/mouse/output/mouseOutput.rds")X. Cluster Biomarkers
Finding differentially expressed features
- min.pct argument: requires a feature to be detected at a minimum percentage in either of the two groups of cells
- thresh.test argument: requires a feature to be differentially expressed (on average) by some amount between the two groups
- to speed up computations: max.cells.per.ident
#some violin plots to check on how the features (PCs) expressed in the corresponding clusters
cluster0.markers <- FindMarkers(mouse, ident.1 = 0, min.pct = 0.25)## For a more efficient implementation of the Wilcoxon Rank Sum Test,
## (default method for FindMarkers) please install the limma package
## --------------------------------------------
## install.packages('BiocManager')
## BiocManager::install('limma')
## --------------------------------------------
## After installation of limma, Seurat will automatically use the more
## efficient implementation (no further action necessary).
## This message will be shown once per sessionhead(cluster0.markers, n = 5)## p_val avg_log2FC pct.1 pct.2 p_val_adj
## Col3a1 0 0.6524410 1.000 0.955 0
## Col5a2 0 0.6328767 0.969 0.733 0
## Col6a3 0 0.6188054 0.952 0.726 0
## Tnn 0 0.9815336 0.507 0.239 0
## Fcer1g 0 -2.2622720 0.153 0.404 0VlnPlot(mouse, features = c("Col3a1", "Col5a2", "Col6a3", "Tnn", "Col5a1")) #plot first 5
cluster1.markers <- FindMarkers(mouse, ident.1 = 1, min.pct = 0.25)
head(cluster1.markers, n = 5)## p_val avg_log2FC pct.1 pct.2 p_val_adj
## Col3a1 0 1.1273284 1.000 0.960 0
## Col5a2 0 0.8307832 0.982 0.760 0
## Fmod 0 1.2055963 0.311 0.059 0
## Angptl1 0 1.1364055 0.325 0.066 0
## Dpt 0 1.2583296 0.705 0.317 0VlnPlot(mouse, features = c("Rpl7", "Rpl31", "Col3a1", "Col5a2", "Spats2l"))
cluster2.markers <- FindMarkers(mouse, ident.1 = 2, min.pct = 0.25)
head(cluster2.markers, n = 5)## p_val avg_log2FC pct.1 pct.2 p_val_adj
## Ptpn18 0 2.113765 0.567 0.152 0
## Col3a1 0 -2.869369 0.839 0.984 0
## Col5a2 0 -2.367420 0.254 0.867 0
## Fn1 0 -1.693357 0.282 0.825 0
## Serpine2 0 -1.940214 0.085 0.496 0VlnPlot(mouse, features = c("Col3a1", "Des", "Serpine2", "Rgs5", "Rgs4"))
cluster3.markers <- FindMarkers(mouse, ident.1 = 3, min.pct = 0.25)
head(cluster3.markers, n = 5)## p_val avg_log2FC pct.1 pct.2 p_val_adj
## Des 0 2.002776 0.479 0.062 0
## Serpine2 0 2.284482 0.887 0.401 0
## Rgs5 0 3.390078 0.961 0.235 0
## Gm13889 0 2.771065 0.724 0.129 0
## Cox4i2 0 1.919825 0.384 0.052 0VlnPlot(mouse, features = c("Col3a1", "Col5a2", "Selp", "F11r", "Cd34")) #"Selp", "F11r", "Cd34" very significant
cluster4.markers <- FindMarkers(mouse, ident.1 = 4, min.pct = 0.25)
head(cluster4.markers, n = 5)## p_val avg_log2FC pct.1 pct.2 p_val_adj
## Il1b 0.000000e+00 1.0356642 0.791 0.353 0.000000e+00
## Tyrobp 0.000000e+00 0.5569658 0.879 0.365 0.000000e+00
## Fcer1g 2.484663e-299 0.4255137 0.765 0.301 4.246289e-295
## Fth1 1.136707e-291 0.3869542 0.999 0.983 1.942632e-287
## Lyz2 6.863201e-278 0.3131246 0.756 0.359 1.172921e-273VlnPlot(mouse, features = c("Col3a1", "Mdk", "Tmsb4x", "Eln", "Col1a2"))
cluster2.markers <- FindMarkers(mouse, ident.1 = 2, min.pct = 0.25)
head(cluster2.markers, n = 5)## p_val avg_log2FC pct.1 pct.2 p_val_adj
## Ptpn18 0 2.113765 0.567 0.152 0
## Col3a1 0 -2.869369 0.839 0.984 0
## Col5a2 0 -2.367420 0.254 0.867 0
## Fn1 0 -1.693357 0.282 0.825 0
## Serpine2 0 -1.940214 0.085 0.496 0cluster5.markers <- FindMarkers(mouse, ident.1 = 5, ident.2 = c(0, 3), min.pct = 0.25)
head(cluster5.markers, n = 5)## p_val avg_log2FC pct.1 pct.2 p_val_adj
## 2700094K13Rik 0 1.384453 0.783 0.263 0
## Ccdc34 0 1.218645 0.559 0.118 0
## Ube2c 0 1.522594 0.405 0.027 0
## Cks1b 0 1.299158 0.634 0.165 0
## H2afz 0 1.588176 0.979 0.660 0mouse.markers <- FindAllMarkers(mouse, only.pos = TRUE, min.pct = 0.25, logfc.threshold = 0.25) #No features pass logfc.threshold threshold, maybe threshold=0.1, 0.15, 0.2?## Calculating cluster 0## Calculating cluster 1## Calculating cluster 2## Calculating cluster 3## Calculating cluster 4## Calculating cluster 5## Calculating cluster 6## Calculating cluster 7## Calculating cluster 8## Calculating cluster 9## Calculating cluster 10## Calculating cluster 11## Calculating cluster 12## Calculating cluster 13## Calculating cluster 14mouse.markers <- FindAllMarkers(mouse, only.pos = TRUE, min.pct = 0.25, logfc.threshold = 0.2) #0.2 is chosen here## Calculating cluster 0## Calculating cluster 1## Calculating cluster 2## Calculating cluster 3## Calculating cluster 4## Calculating cluster 5## Calculating cluster 6## Calculating cluster 7## Calculating cluster 8## Calculating cluster 9## Calculating cluster 10## Calculating cluster 11## Calculating cluster 12## Calculating cluster 13## Calculating cluster 14mouse.markers %>%
group_by(cluster) %>%
slice_max(n = 2, order_by = avg_log2FC)## # A tibble: 30 × 7
## # Groups: cluster [15]
## p_val avg_log2FC pct.1 pct.2 p_val_adj cluster gene
## <dbl> <dbl> <dbl> <dbl> <dbl> <fct> <chr>
## 1 1.01e-71 1.58 0.283 0.181 1.72e-67 0 Saa3
## 2 0 1.47 0.876 0.462 0 0 Crabp1
## 3 0 2.13 0.901 0.411 0 1 Ptn
## 4 0 2.05 0.786 0.312 0 1 H19
## 5 0 4.15 0.813 0.431 0 2 Apoe
## 6 0 4.12 0.692 0.258 0 2 Cd74
## 7 0 3.39 0.961 0.235 0 3 Rgs5
## 8 0 2.77 0.724 0.129 0 3 Gm13889
## 9 1.69e-52 1.20 0.296 0.165 2.90e-48 4 S100a8
## 10 1.52e-56 1.16 0.287 0.153 2.59e-52 4 S100a9
## # … with 20 more rowsFeatures in CellChat paper supplimentary Fig.2.b
VlnPlot(mouse, features = c("Crabp1", "Des", "Rgs5", "C1qb", "Pf4", "Eln", "Ogn", "Cd93", "Pecam1"))
VlnPlot(mouse, features = c("Birc5", "Ccnb2", "Icos", "Nkg7", "Ccr7", "H2-DMb1", "Hdc", "G0s2", "Cadm4"))
VlnPlot(mouse, features = c("Itih5", "Hba-a2", "Hbb-bs", "Acp5", "Mmp9", "Ccl21a", "Lyve1"))
FeaturePlot shows the distribution of expression of features in the clusters
#found after PCA:
FeaturePlot(mouse, features = c("Sparc", "Tyrobp", "Igfbp7", "Pecam1", "Cd34", "Mfap4", "Ndufa4l2", "Il6",
"Birc5"))
#features from paper supplm. fig. 2.b
FeaturePlot(mouse, features = c("Crabp1", "Des", "Rgs5", "C1qb", "Pf4", "Eln", "Ogn", "Cd93", "Pecam1"))
FeaturePlot(mouse, features = c("Birc5", "Ccnb2", "Icos", "Nkg7", "Ccr7", "H2-DMb1", "Hdc", "G0s2", "Cadm4"))
FeaturePlot(mouse, features = c("Itih5", "Hba-a2", "Hbb-bs", "Acp5", "Mmp9", "Ccl21a", "Lyve1"))
mouse.markers %>%
group_by(cluster) %>%
top_n(n = 10, wt = avg_log2FC) -> top10
#DoHeatmap(mouse, features = top10$gene) + NoLegend()
DoHeatmap(mouse, features = top10$gene) 
XI. Assigning Cell Type Identity to Clusters
alternative way to name IDs:
mouse <- RenameIdents(mouse, `0` = "0_FIB-1", `1` = "1_FIB-3", `2` = "2_MYL",
`3` = "3_FIB-2", `4` = "4_undecided", `5` = "5_LYME", `6` = "6_FIB-4", `7` = "7_undecided", `8` = "8_ENDO", `9` = "9_undecided",
`10` = "10_undecided", `11` = "11_SCH", `12` = "12_DEN")
DimPlot(mouse, label = TRUE)
Or directly writing tags
new.cluster.ids <- c(“FIB-1”, “FIB-3”, “MYL”, “FIB-2”, “4”, “ENDO or LYME”, “FIB-4”, “7”, “ENDO”, “9”, “10”, “SCH”, “DEN”) names(new.cluster.ids) <- levels(mouse) mouse <- RenameIdents(mouse, new.cluster.ids) DimPlot(mouse, reduction = “umap”, label = TRUE, pt.size = 0.5) #DimPlot(mouse, reduction = “umap”, label = TRUE, pt.size = 0.5) + NoLegend()
some maybe useful codes (not needed for right now):
#FindConservedMarkers(mouse, idents.1= [nr.] )
#Idents(mouse)
#RenameIdents(mouse, '[nr.]'= [celltype] )dot plot to gain an overview:
markers.to.plot <- c("Crabp1", "Des", "Rgs5", "C1qb", "Pf4", "Eln", "Ogn", "Cd93", "Pecam1","Birc5", "Ccnb2", "Icos", "Nkg7", "Ccr7", "H2-DMb1", "Hdc", "G0s2", "Cadm4", "Itih5", "Hba-a2", "Hbb-bs", "Acp5", "Mmp9", "Ccl21a", "Lyve1")
DotPlot(
mouse,
assay = NULL,
features = markers.to.plot,
cols = c("lightgrey", "blue"),
col.min = -2.5,
col.max = 2.5,
dot.min = 0,
dot.scale = 6,
idents = NULL,
group.by = NULL,
split.by = NULL,
cluster.idents = FALSE,
scale = TRUE,
scale.by = "radius",
scale.min = NA,
scale.max = NA
) + RotatedAxis() 